The disclosed systems, structures, and methods are directed to receiving a training data set comprising a plurality of original training samples, augmenting the original training samples by applying default transformations, training the machine learning model on at least a portion of the original training samples and at least a portion of the first set of augmented training samples, computing an unaugmented accuracy, augmenting the original training samples and the first set of augmented training samples by applying a candidate transformation, training the machine learning model on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, and at least a portion of the second set of augmented training samples, computing an augmented accuracy, computing an affinity metric from the unaugmented accuracy and the augmented accuracy, and updating the candidate augmentation transformations list and the default augmentation transformations list.
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3. The method of claim 1, wherein applying the default transformation to augment the original training samples includes applying an operation as specified by the default transformation in accordance with operational parameters related to the default transformation and a probability of applying the default transformations.
4. The method of claim 1, wherein applying the candidate transformation to augment the original training samples and the first set of augmented training samples includes applying an operation as specified by the candidate transformation in accordance with operational parameters related to the candidate transformation and a probability of applying the candidate transformations.
6. The method of claim 1 further comprises traversing individually all candidate transformations in the candidate augmentation transformations list and updating the candidate augmentation transformations list and the default augmentation transformations list accordingly.
7. The method of claim 1 further comprises selecting n transformations from the updated default augmentation transformations list and the updated candidate augmentation having top n affinity metrics and training the machine learning model in accordance with the original training samples and training samples augmented using n transformations, where n is an integer number.
8. The method of claim 1, wherein the training of the machine learning model on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, and at least a portion of the second set of augmented training samples begins once a previous training of the machine learning model on at least a portion of the original training samples and at least a portion of the first set of augmented training samples reaches an accuracy between 50-60%.
9. The method of claim 1, wherein the affinity metric is a quantitative difference between the unaugmented accuracy and the augmented accuracy.
11. The method of claim 1, wherein the machine learning model is previously untrained.
12. The method of claim 1, wherein the machine learning model is previously trained on the original training samples.
15. The system of claim 13, wherein applying the default transformation to augment the original training samples includes applying an operation as specified by the default transformation in accordance with operational parameters related to the default transformation and a probability of applying the default transformations.
16. The system of claim 13, wherein applying the candidate transformation to augment the original training samples and the first set of augmented training samples includes applying an operation as specified by the candidate transformation in accordance with operational parameters related to the candidate transformation and a probability of applying the candidate transformations.
18. The system of claim 13 further comprises traversing individually all candidate transformations in the candidate augmentation transformations list and updating the candidate augmentation transformations list and the default augmentation transformations list accordingly.
19. The system of claim 13, wherein the training of the machine learning model on at least a portion of the original training samples, at least a portion of the first set of augmented training samples, and at least a portion of the second set of augmented training samples begins once a previous training of the machine learning model on at least a portion of the original training samples and at least a portion of the first set of augmented training samples reaches an accuracy between 50-60%.
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December 3, 2020
January 30, 2024
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